Bootstrap- and Permutation-Based Inference for the Mann-Whitney Effect for Right-Censored and Tied Data

The Mann-Whitney effect is an intuitive measure for discriminating two survival distributions. Here we analyze various inference techniques for this parameter in a two-sample survival setting with independent right-censoring, where the survival times are even allowed to be discretely distributed. This allows for ties in the data and requires the introduction of normalized versions of Kaplan-Meier estimators from which adequate point estimates are deduced. Asymptotically exact inference procedures based on standard normal, bootstrapand permutation-quantiles are developed and compared in simulations. Here, the asymptotically robust and – under exchangeable data – even finitely exact permutation procedure turned out to be the best. Finally, all procedures are illustrated using a real data set.